This document explores qualitative indicators from an ActivityInfo database that is monitoring Ecuador.
| Indicator count totals | |||||
|---|---|---|---|---|---|
| Nov 2013 to May 2019 | |||||
| Date | Quantity | Select | Single-line text | Multi-line text | % of total data collected |
| Nov 2013 | 141,442 | 30,531 | 0 | 6,309 | 3.54% |
| June 2015 | 1,887,857 | 745,841 | 85,863 | 57,128 | 2.06% |
| Sept 2016 | 3,380,991 | 1,296,548 | 191,640 | 116,184 | 2.33% |
| May 2017 | 4,932,977 | 1,809,419 | 265,196 | 168,599 | 2.35% |
| May 2019 | 12,174,327 | 7,595,829 | 2,683,945 | 915,948 | 3.92% |
From the perspective of ActivityInfo, it shows a clear need for new tools to support analysis of qualitative data as the absolute volume of qualitative data has increased by a factor of 150, and almost doubled as a relative share of all data collected.
The data has been extracted from ActivityInfo and pre-processed to make it ready for the analysis. Check the calls in the R/ directory of the project repository to see how the process went on.
Read the data from the source that has been extracted, cleaned, and transformed. Select the rows where the field type equals to NARRATIVE, this indicates that is a multi-line text field in ActivityInfo. Select these columns and analyze them by comparing and contrasting with other fields types associated with the textual field types.
| partnerName | n | prop | freq |
|---|---|---|---|
| ACNUR | 747 | 0.604 | 60% |
| NRC | 134 | 0.108 | 11% |
| PMA | 88 | 0.071 | 7% |
| UNICEF | 67 | 0.054 | 5% |
| OIM | 62 | 0.050 | 5% |
| UNFPA | 58 | 0.047 | 5% |
| CARE | 27 | 0.022 | 2% |
| Dialogo Diverso | 19 | 0.015 | 2% |
| RET | 12 | 0.010 | 1% |
| ADRA | 11 | 0.009 | 1% |
| Plan Internacional | 5 | 0.004 | 0% |
| PNUD | 3 | 0.002 | 0% |
| UNESCO | 3 | 0.002 | 0% |
The table above shows partner count per each record:
ACNUR has most of the records with a frequency of 60%.
Second, NRC comes with a frequency of 11%. The frequency difference between the partners ACNUR and NRC is 49%.
| subPartnerName | n | prop | freq |
|---|---|---|---|
| HIAS | 584 | 0.472 | 47% |
| ACNUR | 226 | 0.183 | 18% |
| NRC | 137 | 0.111 | 11% |
| OIM | 63 | 0.051 | 5% |
| UNFPA | 57 | 0.046 | 5% |
| ADRA | 36 | 0.029 | 3% |
| CARE | 27 | 0.022 | 2% |
| Dialogo Diverso | 18 | 0.015 | 1% |
| UNICEF | 17 | 0.014 | 1% |
| RET | 13 | 0.011 | 1% |
| Plan Internacional | 5 | 0.004 | 0% |
| SJR | 5 | 0.004 | 0% |
| Alas de Colibri | 4 | 0.003 | 0% |
| Buen Pastor | 4 | 0.003 | 0% |
| Casa Matilde | 4 | 0.003 | 0% |
| Fundación de Mujeres de Sucumbios | 4 | 0.003 | 0% |
| Fundación Tarabita | 4 | 0.003 | 0% |
| Hermanas Salesias | 4 | 0.003 | 0% |
| Hogar de Cristo | 4 | 0.003 | 0% |
| Pastoral Social Cáritas Tulcán | 4 | 0.003 | 0% |
| Patronato | 4 | 0.003 | 0% |
| World Vision | 4 | 0.003 | 0% |
| PNUD | 3 | 0.002 | 0% |
| UNESCO | 3 | 0.002 | 0% |
| JRS Ecuador | 2 | 0.002 | 0% |
The table above shows sub-partner count per each record.
The sub-partner reporting the most is HIAS, which is by 47%.
The rest of the sub-partners have small numbers in the responses, however, they might be reporting much with their partners. We will see this in the next plot.
The plots placed in the tabs below show the proportion of records entered by sub-partners and partners.
518 out of 747 total responses of ACNUR is actually coming from HIAS.
UNICEF has more diversed partners in terms of reporting. 44% of responses of UNICEF comes from HIAS. 25% of reporting comes from the UNICEF itself.
The most diversed partner is PMA. There are 13 partners reporting. HIAS reports 40% of the records.
Those are the total numbers of reporting, not specific to the narratives. In the next section, we count the number of reportings done in the narrative sections.
Not all partners (and sub-partners) enter narrative records.
The table is alphabetically ordered.
| province | canton | n | province.prop | province.freq |
|---|---|---|---|---|
| AZUAY | CUENCA | 24 | 0.046 | 4% |
| BOLIVAR | SAN MIGUEL | 1 | 0.002 | 0% |
| CARCHI | TULCAN | 87 | 0.166 | 16% |
| CHIMBORAZO | RIOBAMBA | 2 | 0.004 | 0% |
| COTOPAXI | LATACUNGA | 3 | 0.006 | 0% |
| EL ORO | HUAQUILLAS | 40 | 0.076 | 7% |
| MACHALA | 7 | 0.013 | 1% | |
| ESMERALDAS | ESMERALDAS | 30 | 0.057 | 5% |
| SAN LORENZO | 24 | 0.046 | 4% | |
| GUAYAS | GUAYAQUIL | 46 | 0.088 | 8% |
| IMBABURA | IBARRA | 55 | 0.105 | 10% |
| LOS RIOS | QUEVEDO | 3 | 0.006 | 0% |
| MANABI | MANTA | 2 | 0.004 | 0% |
| PICHINCHA | QUITO | 99 | 0.189 | 18% |
| SANTO DOMINGO DE LOS TSACHILAS | SANTO DOMINGO | 27 | 0.051 | 5% |
| SUCUMBIOS | LAGO AGRIO | 71 | 0.135 | 13% |
| TUNGURAHUA | AMBATO | 2 | 0.004 | 0% |
| BAÑOS DE AGUA SANTA | 2 | 0.004 | 0% |
Treemap plot showing canton and province reporting frequencies.
First of all, we shorten the names and therefore re code form topics because they appear to be too long and disarray the plots. The re coded table below provides a look up for form labels and their abbreviations:
| i | labelFormsRecode | labelForms |
|---|---|---|
| 1 | Salud | Salud |
| 2 | Alojamiento | Alojamiento Temporal |
| 3 | Necesidades | Necesidades básicas/Otro |
| 4 | Población | Manejo de la información y entrega directa de la información a la población |
| 5 | Socios | Manejo de la información para socios y análisis de las necesidades |
| 6 | VBG | Protección_VBG |
| 7 | Tráfico | Trata_y_tráfico |
| 8 | Educación | Acceso_a_educación |
| 9 | Hábitat | Acceso a vivienda y hábitat dignos en comunidades receptoras |
| 10 | Técnico | Medios de vida y formación técnico-profesional |
| 11 | SocialCohesión | Cohesión_social |
| 12 | Educacional | Apoyo Educacional a Comunidades Receptoras |
| 13 | VBG_SSR | Asistencia técnica para VBG-SSR |
| 14 | Fronteras | Asistencia técnica para protección/gestión de fronteras |
| 15 | Coordinacion | Asistencia técnica para gestion de la informacion y coordinacion |
| 16 | SectorLaboral | Asistencia técnica para el sector laboral |
| 17 | Protección | Asistencia técnica para protección |
| 18 | ProtecciónInfancia | Asistencia técnica para protección de la infancia |
| 19 | LGBTI | Protección_LGBTI |
Response quality means how much response the questions receive. The idea is to find relations that affect the response quality to understand if they work or not under some conditions.
Research questions:
What is the quality of textual responses in the narrative fields?
Is there any relationship between the word counts of response, question and description fields?
What is the distribution between response word count and explanatory variables such as the question, form topic, canton name, partner name, etc.
Assumptions:
In other words, we assume that the more word the better is. The limitations are based on the unequal distribution of the data. The word count of responses and questions can be related to other things, such as the questions require short answers so then the responses tend to be shorter.
Additionally, we can have a cross-analysis to test these outcomes. It might be a good idea to have a small subset of data and ask an expert to test the assumptions qualitatively. For instance, we can take the first twenty responses with the highest word count and the last twenty responses with the lowest word count. We chose the extreme directions because they point out the greatest differences which are easier to test assumptions.
One issue with the nature of the questions is that they are only unique in a form. These questions can be distributed across multiple forms. The questions sharing the same name will have different meanings. For instance, the question “Cualitativo” from the form “Salud” should imply different thing than the question “Cualitativo” from the form “Protección_VBG”.
In order to solve this kind of problem:
We can combine question with the form and also its folder label. There we can achieve a unique name for each question.
Another thing to resolve this would be doing analysis to move the analysis up to form level. In this file, we did both, therefore the analysis shown as below:
Count of responses per topic/question:
| labelForms | question | response | .responseWordCount | .questionWordCount | partnerName | canton | description | labelFormsRecode |
|---|---|---|---|---|---|---|---|---|
| Salud | Cualitativ… | 1. Entrega… | 302 | 1 | UNFPA | MACHALA | Descripció… | Salud |
| Salud | Cualitativ… | 1. Entrega… | 302 | 1 | UNFPA | LAGO AGRIO | Descripció… | Salud |
| Salud | Cualitativ… | 1. Entrega… | 302 | 1 | UNFPA | HUAQUILLAS | Descripció… | Salud |
| Salud | Cualitativ… | 233 Equipo… | 46 | 1 | UNFPA | SAN LORENZO | Descripció… | Salud |
| Salud | Cualitativ… | 1. Entrega… | 302 | 1 | UNFPA | TULCAN | Descripció… | Salud |
| Salud | Cualitativ… | Se complem… | 13 | 1 | UNFPA | LAGO AGRIO | Descripció… | Salud |
It’s also a good practice to see the number of questions. For example, one question has two responses, therefore they’re short. Therefore, jittered points are added to give a glance about the number of observations in the same plot.
Figure: Box plot form topics and response word counts based on the raw data
In the plot above, the outliers are shown in orange color. Outliers are the points placed outside the whiskers, which is the long line, of the boxplot[^1].
The response word count distribution per form topic categorized by partner name:
The response word count distribution per form topic categorized by canton name:
A caveat: Reducing multiple values down to a single value should be avoided in the early stages of the analysis because reducing hides a lot e.g. a bar chart showing average the word count per partner. Some partners may write longer than others, because:
They actually write longer than other partners.
The questions they answered require short answers.
Some questions have the description field giving extra details about the questions.
Do some questions with the extra description field have better response quality than the questions which do not have it?
Looking at the table containing form name, question, description and so on:
We see in the plot below that the response word counts per form and colored if a response has a description field or not. Having a description field or not is calculated as that a description field has a minimum one word.
The responses with the longest word counts are the ones with description. Nevertheless, it is not so easy to see a clear trend that there’s a correlation between response word count and description fields. Interestingly, the questions in the form F15, which is Protección_VBG, has no description fields at all.
We look below the description word count and compare with the response word count (and remove the categorical field displaying if the question of response has a description field).
TODO ANOVA
TODO
We can look at multiple continuous variables in our data.
word count of response field: the dependent variable.
word count of question field: an independent variable.
word count of description field: an independent variable.
Scatter plots help understand the characteristics of those variables. However, we miss a general understanding that is the trend line.
The gray area around the lines shows the confidence band at the 0.95 level. Although there’s a straight slope in the linear regression line, we cannot say that the trend line is robust because the confidence band representing the uncertainty in the estimate is wide.
TODO
In that section, we take text as data.
Silge J, Robinson D (2017). Text mining with R: A tidy approach. O’Reilly Media, Inc.
QualMiner project explores the qualitative data used for Venezuelan refugee response by applying text analysis & mining techniques. The project is funded by the UNHCR Innovation Fund.